Compression of hyperspectral imagery via linear prediction
Francesco Rizzo, Bruno Carpentieri, Giovanni Motta, James A. Storer · Kluwer Academic Publishers eBooks · 2006
Motta et al., 2003) proposed a Locally Optimal Vector Quantizer (LPVQ) for lossless encoding of hyperspectral data, in particular, Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images. In this paper we first show how it is possible to improve the baseline LPVQ algorithm via linear prediction techniques, band reordering and least squares optimization. Then, we use this knowledge to devise a new lossless compression method for AVIRIS images. This method is based on a low complexity, linear prediction approach that exploits the linear nature of the correlation existing between adjacent bands. A simple heuristic is used to detect contexts in which such prediction is likely to perform poorly, thus improving overall compression and requiring only marginal extra storage space. A context modeling mechanism coupled with a one band look ahead capability allows the proposed algorithm to match LPVQ compression performances at a fraction of its space and time requirements. This makes the proposed method suitable to applications where limited hardware is a key requirement, spacecraft on board implementation. We also present a least squares optimized linear prediction for AVIRIS images which, to the best of our knowledge, outperforms any other method published so far.